Random variables are rarely independent in practice and so many multivariate distributions have been proposed in the literature to give a dependence structure for two or more variables. In this book, we restrict ourselves to the bivariate distributions for two reasons: (i) correlation structure and other properties are easier to understand and the joint density plot can be displayed more easily, and (ii) a bivariate distribution can normally be extended to a multivariate one through a vector or matrix representation. This volume is a revision of Chapters 1-17 of the previous book Continous Bivariate Distributions, Empasising Applications authored by Drs. Paul Hutchinson and Chin-Diew Lai.
The book updates the subject of copulas which have grown immensely during the past two decades. Similarly, conditionally specified distributions and skewed distributions have become important topics of discussion in this area of research. The volume, which provides an up-to-date review of various developments relating to bivariate distributions in general, should be of interest to academics and graduate students, as well as applied researchers in finance, economics, science, engineering and technology.
